論文筆記-Training RNNs as Fast as CNNs
摘要 由於在並行狀態計算上的內在困難，RNN架構很難擴展。 比如，h_t 的前向計算被阻止直到 h_(t−1) 的整個計算結束，這對並行計算來說是一個主要的瓶頸。在這項工作中，我們提出了Simple Recurrent Unit (SRU) 架構，簡化了計算並展現更多的並行性。在SRU中，每一步的大部分計算都是獨立的，可以很容易的並行化。SRU和卷積層一樣快，並且優化後的LSTM實現快5-10x。
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相關文章
- 1. Training RNNs as Fast as CNNs
- 2. [論文筆記]：ResGCN-Can GCNs Go as Deep as CNNs?
- 3. 論文筆記：DeepGCNs: Can GCNs Go as Deep as CNNs?
- 4. 閱讀論文 《Train RNN as fast as CNN》
- 5. 小白科研筆記：簡析論文DeepGCNs: Can GCNs Go as Deep as CNNs
- 6. 論文筆記：Object as Points
- 7. 論文筆記——CenterNet：Objects as Points
- 8. DeepGCNs: Can GCNs Go as Deep as CNNs? 論文解析
- 9. ICCV19開源論文 DeepGCNs: Can GCNs Go as Deep as CNNs?
- 10. ICCV2019點雲論文 ResGCN-Can GCNs Go as Deep as CNNs